Comprehensive maps of material stock dynamics reveal increasingly coordinated urban development in the Yangtze River Delta of China

Abstract

Sustainable urban development critically depends on effectively managing the interplay between material stock (MS) and economic growth. This study combined convolutional neural network model and nighttime lights data to map building MS of Yangtze River Delta (YRD) urban agglomeration in China from 2000 to 2020 across 1 km × 1 km pixel scale, then uncovered the spatiotemporal dynamics of MS and its correlation with economic development. Our findings indicate that the model performed robustly on the test set (R² > 0.88). YRD's MS surged over tenfold, reaching 20,772 teragram, primarily expanding along northwest-southeast developmental axes. Most YRD cities exhibited synchronized growth in material stock and GDP, suggesting an emergent pattern of sustainable urban expansion. However, cities at the developmental extremes highlighted the need for optimizing urban development strategies. By categorizing YRD cities into four distinct development modes, our study offers deep insights into the dynamics of urban development, underpinning targeted strategies that could guide cities towards more sustainable and resource-efficient growth trajectories.